Zero Shot Health Trajectory Prediction Using Transformer
This work addresses healthcare cost and complexity by providing a tool for care optimization and bias mitigation, though it appears incremental as an application of transformers to a new domain.
The paper tackles predicting future health trajectories from episodic health data using a transformer-based model called ETHOS, which employs zero-shot learning to eliminate the need for labeled data and fine-tuning, achieving this through tokenized patient health timelines.
Integrating modern machine learning and clinical decision-making has great promise for mitigating healthcare's increasing cost and complexity. We introduce the Enhanced Transformer for Health Outcome Simulation (ETHOS), a novel application of the transformer deep-learning architecture for analyzing high-dimensional, heterogeneous, and episodic health data. ETHOS is trained using Patient Health Timelines (PHTs)-detailed, tokenized records of health events-to predict future health trajectories, leveraging a zero-shot learning approach. ETHOS represents a significant advancement in foundation model development for healthcare analytics, eliminating the need for labeled data and model fine-tuning. Its ability to simulate various treatment pathways and consider patient-specific factors positions ETHOS as a tool for care optimization and addressing biases in healthcare delivery. Future developments will expand ETHOS' capabilities to incorporate a wider range of data types and data sources. Our work demonstrates a pathway toward accelerated AI development and deployment in healthcare.